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Road safety and flood management are major concerns, especially in water-prone areas where pedestrian safety is at risk due to hidden potholes and road hazards to address this issue, a real-time pothole detection and reporting system is proposed, aiming to enhance pedestrian safety during flooding. The system offers many benefits, such as improved road safety, reduced accidents and injuries due to potholes, real-time monitoring, and reporting of road conditions to enable priority action. The application uses the Map API for real-time pothole location tracking and uses the YOLOv8 module, an advanced object detection technique, to identify potholes from images taken by users. Known pothole locations are stored in a database that can be accessed by real-time users and government officials. User feedback and app usage statistics are included in the dataset to evaluate the efficiency of the application and the accuracy of the YOLOv8 module. Ethical considerations such as user data privacy and informed consent are considered to ensure responsible data handling. The proposed machine's contribution to society is tremendous, with capacity benefits such as stepped-forward street protection, decreased vehicle damage and repair prices, extended performance in avenue protection, better travel experiences, and effective environmental impacts from reduced fuel intake and emissions. The machine's destiny scope consists of incorporating additional features like actual-time traffic statistics, street closures, and protecting greater avenue hazards inclusive of debris and flooding. Machine mastering and AI technology may be in addition employed to enhance pothole detection precision. Overall, this research paper provides a comprehensive method for tackling road safety issues in the course of floods through a value-effective and green Real-time Pothole Detection and Notification System. The ability effect of this machine on pedestrian protection and avenue protection, at the side of destiny improvements, gives promising possibilities for a smarter and safer transportation network.

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An AI and ML-Enhanced Approach with IoT for Pothole Detection to Ensure Pedestrian Safety During Flooding

  • 会议论文

  • 发表日期：

    2025年

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  • Pancham Singh,
  • Mrignainy Kansal,
  • Purvika Panwar,
  • Manas Mishra,
  • Kartikeya Patel,
  • Avinash Kumar Sharma

摘要

Road safety and flood management are major concerns, especially in water-prone areas where pedestrian safety is at risk due to hidden potholes and road hazards to address this issue, a real-time pothole detection and reporting system is proposed, aiming to enhance pedestrian safety during flooding. The system offers many benefits, such as improved road safety, reduced accidents and injuries due to potholes, real-time monitoring, and reporting of road conditions to enable priority action. The application uses the Map API for real-time pothole location tracking and uses the YOLOv8 module, an advanced object detection technique, to identify potholes from images taken by users. Known pothole locations are stored in a database that can be accessed by real-time users and government officials. User feedback and app usage statistics are included in the dataset to evaluate the efficiency of the application and the accuracy of the YOLOv8 module. Ethical considerations such as user data privacy and informed consent are considered to ensure responsible data handling. The proposed machine's contribution to society is tremendous, with capacity benefits such as stepped-forward street protection, decreased vehicle damage and repair prices, extended performance in avenue protection, better travel experiences, and effective environmental impacts from reduced fuel intake and emissions. The machine's destiny scope consists of incorporating additional features like actual-time traffic statistics, street closures, and protecting greater avenue hazards inclusive of debris and flooding. Machine mastering and AI technology may be in addition employed to enhance pothole detection precision. Overall, this research paper provides a comprehensive method for tackling road safety issues in the course of floods through a value-effective and green Real-time Pothole Detection and Notification System. The ability effect of this machine on pedestrian protection and avenue protection, at the side of destiny improvements, gives promising possibilities for a smarter and safer transportation network.